In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.
|Title of host publication||Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2016 Dec 9|
|Event||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States|
Duration: 2016 Jun 26 → 2016 Jul 1
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Period||16/6/26 → 16/7/1|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition